Touch-up is the art of removing unwanted features from an image. Touch-up tools are now key feature in many high-end software based photo-editing products. However, the touch-up process is far from automatic. Typically, a user must manually identify and select unwanted features or blemishes, figure out which pixel operations will render the features invisible, and coordinate many such edits so that the final image looks artifact-free.
Because the human eye is much more likely to notice natural blemishes and digital manipulations of images of faces than of general scenes, most touch-up work is done on images of face. However, even working with advanced touch-up tools can be onerous. It is desired to fully automate touch-up inside a camera so that the output image of the camera is actually blemish free.
A common premise of non-photorealistic rendering (NPR) and super-resolution (SR) has the following assumptions. If the surfaces constituting a scene are known, then it becomes possible to determine how the image would appear with small changes to lighting, geometry, and materials. It is significant that touch-up has been more successful in NPR images than in SR images.
One prior art method uses a Markov random field (MRF) that generates a distribution over whole-image patch labelings on the basis of image content and patch-adjacency statistics observed in training images. A maximum a posteriori (MAP) labeling determines new textures. Parameter estimation and inference in MRFs ranges from hard to NP-hard,
It is unlikely that a true scene can be estimated precisely and reliably, especially from impromptu images of faces snapped by teenagers with camera equipped mobile phones, especially in harshly lit indoor scenes.